Fire_summary

Abstract

This webpage will show the results of a bat survey study done in the Plumas National Forest in North California. The objective of this study is to determine the distribution of the different species of bats within the park. In order to do that we have performed occupancy models for the species present in the park. The results of this models will be shown as maps showing the probability of occurence of bats in each point, that is, if you see a value of 1, there is a 100% chance of finding a bat in that point, if there is a value of 0 there is 0% chance of finding that specie in that point, if there is a value of 0.5 there is a 50% chance of finding that specie in that point.

Another result

Results collected in the field

Maps showing the sampled Points

Results of species prescence

In this area 0 means absence, and 1 means prescence. This table has for each site (ID), every specie and day, so for example if Mylu1=0, that means that for Myotis lucifugus (common name Little Brown bat, was detected on day one for that particular site).

Here is a key for bat species

• Myotis yumanensis (Myyu)
• Myotis californicus (Myca)
• Myotis ciliolabrum (Myci)
• Myotis volans (Myvo)
• Myotis lucifugus (Mylu)
• Parastrellus hesperus (Pahe)
• Lasiurus blossevillii (Labo)
• Myotis evotis (Myev)
• Antrozous pallidus (Anpa)
• Eptesicus fuscus (Epfu)
• Lasionycteris noctivagans (Lano)
• Myotis thysanodes (Myth)
• Tadarida brasiliensis (Tabr)
• Lasiurus cinereus (Laci)
• Corynorhinus townsendii (Coto)
• Euderma maculatum (Euma)
• Eumops perotis (Eupe)

Maps predicting the distribution of bats

Yuma myotis (Myotis yumanensis)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
psi(Int)	-1.23	-1.22	-1.32	-1.41	-1.68	-1.80	-1.23	-1.59
	(1.04)	(1.06)	(1.06)	(1.05)	(1.10)	(1.12)	(1.04)	(1.09)
psi(Burn.intensity.basal)	151.57					145.77		131.30
	(203.26)							(200.99)
p(Int)	-3.84***	-4.15***	-3.93***	-3.48*	-3.89***	-3.92***	-3.23*	-3.75**
	(1.17)	(1.23)	(1.19)	(1.35)	(1.18)	(1.18)	(1.32)	(1.41)
p(Meantemp)	0.13	0.15*	0.14	0.16*	0.14	0.14	0.15	0.19*
	(0.07)	(0.07)	(0.07)	(0.08)	(0.07)	(0.07)	(0.08)	(0.08)
psi(Burn.intensity.Canopy)		102.87		185.56	152.69
		(199.23)		(208.80)	(16777.81)
psi(Burn.intensity.soil)			69.41		166.50	163.31	116.72
			(205.06)		(16777.82)		(198.86)
p(sdtemp)				-0.29			-0.31	-0.34
				(0.31)			(0.31)	(0.33)
Log Likelihood	-33.43	-33.65	-33.87	-32.74	-32.89	-32.91	-33.08	-33.10
AICc	75.83	76.28	76.73	76.99	77.29	77.32	77.66	77.70
Delta	0.00	0.46	0.90	1.16	1.46	1.49	1.84	1.87
Weight	0.07	0.05	0.04	0.04	0.03	0.03	0.03	0.03
Num. obs.	46	46	46	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

California bat (Myotis californicus)

Total model

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13	Model 14	Model 15	Model 16
psi(Int)	1.02**	0.58	0.57	1.02**	0.60	0.54	1.06**	0.67	0.65	0.67	0.67	0.70	0.49	1.03**	0.58	0.58
	(0.35)	(0.44)	(0.43)	(0.35)	(0.44)	(0.45)	(0.37)	(0.43)	(0.43)	(0.43)	(0.44)	(0.44)	(0.44)	(0.36)	(0.44)	(0.44)
p(Int)	2.29**	2.30**	3.12**	3.07**	-0.88	-1.01	-0.73	2.30**	3.12**	3.12**	-0.87	-0.87	0.43	0.98	0.76	-0.46
	(0.77)	(0.77)	(0.98)	(0.98)	(0.82)	(0.79)	(0.81)	(0.77)	(0.98)	(0.98)	(0.82)	(0.82)	(1.47)	(1.61)	(1.63)	(0.90)
p(Meanhum)	-0.02*	-0.02*	-0.03*	-0.02*				-0.02*	-0.03*	-0.03*			-0.01	-0.02	-0.01
	(0.01)	(0.01)	(0.01)	(0.01)				(0.01)	(0.01)	(0.01)			(0.01)	(0.01)	(0.01)
psi(Burn.intensity.soil)		0.37	0.41		0.44	8.55							7.31		0.41	0.48
		(0.27)	(0.29)		(0.32)	(6.13)							(4.85)		(0.30)	(0.33)
p(sdhum)			-0.06	-0.06					-0.06	-0.06						-0.04
			(0.04)	(0.04)					(0.04)	(0.04)						(0.04)
p(Meantemp)					0.12*	0.12*	0.11				0.12*	0.12*	0.08	0.06	0.07	0.11*
					(0.06)	(0.05)	(0.06)				(0.06)	(0.06)	(0.06)	(0.07)	(0.07)	(0.06)
psi(Burn.intensity.Canopy)						-6.74		0.28	0.34		0.36
						(4.96)		(0.25)	(0.28)		(0.31)
psi(Burn.intensity.basal)										0.24		0.25	-4.20
										(0.21)		(0.23)	(2.84)
Log Likelihood	-82.47	-81.35	-80.11	-81.39	-81.53	-80.29	-82.86	-81.67	-80.41	-80.50	-81.87	-81.96	-79.41	-82.06	-80.80	-80.87
AICc	171.52	171.68	171.72	171.76	172.03	172.09	172.28	172.32	172.32	172.49	172.72	172.90	172.98	173.09	173.11	173.25
Delta	0.00	0.16	0.20	0.24	0.51	0.57	0.77	0.81	0.81	0.98	1.20	1.38	1.46	1.58	1.59	1.73
Weight	0.03	0.03	0.03	0.03	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.01	0.01	0.01
Num. obs.	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

Western Small Footed Myotis (Myotis ciliolabrum)

Statistical models

	Model 1	Model 2	Model 3
psi(Int)	-2.23*	-2.41*	-2.34*
	(0.98)	(1.04)	(1.02)
psi(Burn.intensity.basal)	-75.21	-138.72	-122.96
	(68.15)	(98.83)	(91.89)
psi(Burn.intensity.Canopy)	121.93	226.24	200.39
	(111.94)	(162.42)	(151.00)
psi(Burn.intensity.soil)	-20.25	-39.44	-34.72
	(20.71)	(30.09)	(27.97)
p(Int)	3.56*	4.34*	3.60*
	(1.44)	(1.86)	(1.57)
p(Meantemp)	-0.31**	-0.33**	-0.31**
	(0.10)	(0.11)	(0.10)
p(sdhum)		-0.05
		(0.08)
p(sdtemp)			-0.01
			(0.28)
Log Likelihood	-31.97	-30.93	-31.29
AICc	78.08	78.80	79.53
Delta	0.00	0.72	1.45
Weight	0.17	0.12	0.08
Num. obs.	46	46	46
p < 0.001, p < 0.01, p < 0.05

Hairy-winged bat (Myotis volans)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5
psi(Int)	-0.04	-0.45	-0.73	0.38	0.05
	(0.95)	(0.71)	(0.61)	(1.15)	(1.01)
p(Int)	4.10	-0.28	-2.14	2.53	4.92
	(4.32)	(1.06)	(1.70)	(3.89)	(4.28)
p(Julian)	-0.03			-0.02	-0.03
	(0.02)			(0.02)	(0.02)
p(Meanhum)		-0.01			-0.01
		(0.01)			(0.01)
p(Meantemp)			0.09
			(0.14)
psi(Burn.intensity.basal)				-33.38
				(30.21)
psi(Burn.intensity.Canopy)				44.81
				(40.51)
Log Likelihood	-40.67	-40.92	-41.12	-38.70	-40.21
AICc	87.92	88.42	88.81	88.90	89.40
Delta	0.00	0.50	0.89	0.98	1.47
Weight	0.11	0.08	0.07	0.07	0.05
Num. obs.	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

Little Brown bat (Myotis lucifugus)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13
psi(Int)	-0.93*	-0.92*	-0.95*	-0.45	-0.95*	-0.44	-0.53	-0.84	-0.93*	-0.85	-0.81	-0.94*	-0.53
	(0.45)	(0.44)	(0.46)	(0.34)	(0.44)	(0.35)	(0.32)	(0.45)	(0.43)	(0.46)	(0.45)	(0.45)	(0.33)
psi(Burn.intensity.Canopy)	0.42				0.31			0.31
	(0.25)				(0.20)			(0.22)
p(Int)	6.21	6.33	6.95	5.25	-3.53*	7.10	-3.14	8.20	-3.57*	8.62	8.26	-3.47*	-1.18
	(5.19)	(5.19)	(5.27)	(5.27)	(1.69)	(5.25)	(1.61)	(5.38)	(1.70)	(5.54)	(5.40)	(1.67)	(1.10)
p(Julian)	-0.06*	-0.06*	-0.06*	-0.05		-0.05		-0.05		-0.06	-0.05
	(0.03)	(0.03)	(0.03)	(0.03)		(0.03)		(0.03)		(0.03)	(0.03)
p(Meantemp)	0.22*	0.22*	0.22*	0.19	0.20		0.18		0.20			0.19
	(0.11)	(0.11)	(0.11)	(0.11)	(0.11)		(0.11)		(0.11)			(0.11)
p(sdtemp)	0.57	0.59	0.58	0.56	0.56*	0.73	0.56	0.74*	0.57*	0.75*	0.75*	0.57*	0.68
	(0.32)	(0.32)	(0.32)	(0.33)	(0.27)	(0.37)	(0.29)	(0.36)	(0.27)	(0.36)	(0.37)	(0.28)	(0.35)
psi(Burn.intensity.basal)		0.32							0.22		0.22
		(0.20)							(0.15)		(0.17)
psi(Burn.intensity.soil)			0.48							0.35		0.31
			(0.29)							(0.26)		(0.22)
Log Likelihood	-48.87	-48.89	-48.96	-50.73	-50.88	-52.15	-52.15	-50.95	-50.96	-51.02	-51.04	-51.09	-53.58
AICc	111.90	111.94	112.08	112.97	113.26	113.27	113.28	113.39	113.42	113.53	113.59	113.68	113.72
Delta	0.00	0.04	0.18	1.06	1.35	1.37	1.38	1.49	1.52	1.63	1.69	1.78	1.82
Weight	0.04	0.04	0.04	0.03	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02
Num. obs.	46	46	46	46	46	46	46	46	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

Western Red Bat (Lasiurus blossevillii)

Statistical models

	Model 1	Model 2	Model 3
psi(Int)	3.62	4.11	16.05
	(12.36)	(18.09)	(17.95)
p(Int)	7.34	6.54	6.82
	(4.13)	(4.30)	(4.22)
p(Julian)	-0.05*	-0.06*	-0.05*
	(0.02)	(0.02)	(0.02)
p(Meantemp)		0.08
		(0.08)
psi(Burn.intensity.Canopy)			18.92
			(23.46)
psi(Burn.intensity.soil)			-26.43
			(32.33)
Log Likelihood	-35.47	-35.01	-33.82
AICc	77.51	79.00	79.13
Delta	0.00	1.49	1.62
Weight	0.08	0.04	0.03
Num. obs.	46	46	46
p < 0.001, p < 0.01, p < 0.05

Long-eared Bat (Myotis evotis)

Total model

Statistical models

	Model 1	Model 2	Model 3	Model 4
psi(Int)	0.68	0.71	0.75	1.31**
	(0.51)	(0.49)	(0.54)	(0.45)
psi(Burn.intensity.basal)	-8.76		-28.35
	(10.44)		(52.78)
psi(Burn.intensity.Canopy)	13.29		44.74
	(16.49)		(85.78)
p(Int)	1.22*	1.25*	2.60*	1.13
	(0.61)	(0.62)	(1.24)	(0.63)
p(Meanhum)	-0.01	-0.01	-0.02*	-0.01
	(0.01)	(0.01)	(0.01)	(0.01)
psi(Burn.intensity.soil)		0.70
		(0.56)
p(Meantemp)			-0.07
			(0.05)
Log Likelihood	-85.66	-87.18	-84.87	-89.00
AICc	182.81	183.33	183.90	184.57
Delta	0.00	0.52	1.09	1.76
Weight	0.13	0.10	0.07	0.05
Num. obs.	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

Pallid Bat (Antrozous pallidus)

Statistical models

	Model 1	Model 2	Model 3
psi(Int)	-0.83	-1.14	-1.05
	(0.84)	(0.74)	(0.78)
psi(Burn.intensity.Canopy)	72.60	18.52	30.46
	(130.37)	(13.35)	(40.77)
psi(Burn.intensity.soil)	-86.32	-21.35	-35.65
	(156.22)	(15.76)	(48.94)
p(Int)	13.69*	7.91	8.69
	(6.07)	(4.67)	(5.05)
p(Julian)	-0.07*	-0.05	-0.06*
	(0.03)	(0.03)	(0.03)
p(Meantemp)	-0.20
	(0.12)
p(Meanhum)			0.02
			(0.02)
Log Likelihood	-25.76	-27.54	-26.35
AICc	65.68	66.57	66.85
Delta	0.00	0.90	1.18
Weight	0.19	0.12	0.10
Num. obs.	46	46	46
p < 0.001, p < 0.01, p < 0.05

Fringed Bat (Myotis thysanoides)

Statistical models

	Model 1	Model 2
psi(Int)	-1.93*	-1.99*
	(0.79)	(0.79)
psi(Burn.intensity.basal)	42.88	50.65
	(29.30)	(35.10)
psi(Burn.intensity.Canopy)	-78.13	-92.09
	(51.58)	(62.19)
psi(Burn.intensity.soil)	24.28	28.39
	(14.59)	(17.88)
p(Int)	-5.56**	-6.56**
	(1.76)	(2.20)
p(Meantemp)	0.32*	0.35**
	(0.13)	(0.13)
p(sdtemp)		0.22
		(0.27)
Log Likelihood	-26.59	-26.17
AICc	67.34	69.29
Delta	0.00	1.95
Weight	0.14	0.05
Num. obs.	46	46
p < 0.001, p < 0.01, p < 0.05

Townsend’s Long-eared Bat (Corynorhinus townsendii)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13	Model 14
psi(Int)	-0.92	-0.27	0.27	-0.85	-1.31	-1.13	0.06	-1.08	-1.11	-1.63	-0.73	-0.65	-0.69	0.23
	(1.35)	(1.61)	(2.14)	(1.07)	(1.16)	(1.46)	(1.88)	(1.46)	(1.07)	(1.24)	(1.36)	(1.37)	(1.40)	(1.84)
psi(Burn.intensity.basal)	-51.10				-51.32	-23.85				-18.75		0.78
	(48.85)				(41.90)	(31.11)				(20.57)		(1.08)
psi(Burn.intensity.Canopy)	70.07				70.52			-34.60			0.91
	(66.72)				(57.35)			(37.55)			(1.11)
p(Int)	-4.73**	-4.38*	-8.63*	6.23	5.17	-4.39*	0.46	-4.26*	-2.19	5.54	-9.13**	-9.15**	-9.10**	-0.07
	(1.77)	(2.13)	(3.76)	(7.28)	(7.13)	(1.74)	(6.52)	(1.73)	(1.76)	(5.77)	(3.54)	(3.50)	(3.52)	(7.79)
p(Meanhum)	0.03	0.03	0.05		0.03	0.03	0.03	0.03			0.05	0.05	0.05	0.05
	(0.02)	(0.02)	(0.03)		(0.02)	(0.02)	(0.02)	(0.02)			(0.03)	(0.03)	(0.03)	(0.03)
p(Meantemp)			0.16						0.05		0.19	0.19	0.19	0.17
			(0.11)						(0.10)		(0.11)	(0.11)	(0.11)	(0.12)
p(Julian)				-0.04	-0.05		-0.03			-0.04				-0.05
				(0.04)	(0.04)		(0.03)			(0.03)				(0.04)
psi(Burn.intensity.soil)						40.49		42.88		31.99			0.92
						(51.88)		(46.24)		(34.12)			(1.30)
Log Likelihood	-20.41	-22.90	-21.91	-23.18	-19.48	-21.05	-22.43	-21.20	-23.70	-21.25	-21.31	-21.36	-21.36	-21.39
AICc	52.31	52.37	52.80	52.93	53.11	53.59	53.84	53.90	53.98	54.01	54.12	54.22	54.23	54.27
Delta	0.00	0.06	0.48	0.62	0.80	1.28	1.53	1.59	1.67	1.69	1.81	1.90	1.91	1.96
Weight	0.07	0.06	0.05	0.05	0.04	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.03	0.02
Num. obs.	46	46	46	46	46	46	46	46	46	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

The western pipistrelle (Parastrellus hesperus)

big brown bat (Eptesicus fuscus)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
psi(Int)	-1.07*	-0.97*	-0.95*	-0.95	-0.86	-1.08*	-1.02*	-0.83
	(0.49)	(0.46)	(0.46)	(0.52)	(0.50)	(0.49)	(0.50)	(0.49)
psi(Burn.intensity.soil)	0.53*			0.51*		0.53*	0.53*
	(0.24)			(0.24)		(0.23)	(0.24)
p(Int)	2.02*	2.03*	2.04*	3.94	4.02*	1.61	-2.14	3.95
	(1.01)	(1.01)	(1.01)	(2.01)	(2.01)	(1.07)	(4.77)	(2.02)
p(Meanhum)	-0.03*	-0.03*	-0.03*	-0.04*	-0.04*	-0.03*	-0.03*	-0.04*
	(0.01)	(0.01)	(0.01)	(0.02)	(0.02)	(0.01)	(0.01)	(0.02)
psi(Burn.intensity.Canopy)		0.45*			0.44*
		(0.21)			(0.22)
psi(Burn.intensity.basal)			0.33*					0.32
			(0.16)					(0.16)
p(Meantemp)				-0.11	-0.12			-0.11
				(0.10)	(0.10)			(0.10)
p(sdhum)						0.05
						(0.06)
p(Julian)							0.02
							(0.02)
Log Likelihood	-57.63	-57.84	-57.91	-57.02	-57.17	-57.18	-57.24	-57.30
AICc	124.25	124.66	124.79	125.54	125.84	125.87	125.98	126.10
Delta	0.00	0.41	0.55	1.30	1.59	1.62	1.74	1.85
Weight	0.06	0.05	0.05	0.03	0.03	0.03	0.03	0.02
Num. obs.	46	46	46	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

silver-haired bat (Lasionycteris noctivagans)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9	Model 10	Model 11	Model 12	Model 13	Model 14	Model 15	Model 16	Model 17	Model 18	Model 19	Model 20	Model 21	Model 22
psi(Int)	-0.29	-0.30	-0.34	-0.05	-0.23	-0.27	-0.22	-0.30	-0.31	-0.35	-0.28	-0.32	-0.27	-0.28	-0.33	-0.29	0.28	0.19	0.20	0.16	0.19	-0.25
	(0.41)	(0.41)	(0.42)	(0.56)	(0.44)	(0.45)	(0.43)	(0.40)	(0.41)	(0.42)	(0.41)	(0.43)	(0.41)	(0.41)	(0.42)	(0.41)	(0.37)	(0.32)	(0.32)	(0.31)	(0.32)	(0.43)
psi(Burn.intensity.basal)	0.27			-72.83			0.25	0.25					0.25	0.26
	(0.16)			(85.88)			(0.15)	(0.15)					(0.15)	(0.15)
p(Int)	0.91	0.91	0.91	6.30*	1.31	1.28	1.25	1.02	1.02	1.02	0.19	0.20	0.19	0.95	0.93	0.91	1.45	0.89	0.14	1.04	0.74	1.65
	(0.48)	(0.48)	(0.48)	(3.07)	(1.04)	(1.04)	(1.04)	(0.79)	(0.79)	(0.79)	(0.72)	(0.72)	(0.72)	(3.12)	(3.12)	(3.11)	(1.04)	(0.49)	(0.74)	(0.79)	(3.17)	(1.11)
p(sdhum)	-0.04	-0.04	-0.04															-0.03				-0.04
	(0.04)	(0.04)	(0.04)															(0.04)				(0.04)
psi(Burn.intensity.Canopy)		0.36		102.16	0.35				0.34		0.34					0.35						0.36
		(0.21)		(120.18)	(0.21)				(0.20)		(0.21)					(0.21)						(0.22)
psi(Burn.intensity.soil)			0.39			0.38				0.38		0.38			0.39
			(0.22)			(0.23)				(0.22)		(0.22)			(0.22)
p(Julian)				-0.02										-0.00	-0.00	-0.00					-0.00
				(0.02)										(0.02)	(0.02)	(0.02)					(0.02)
p(Meantemp)				-0.15**	-0.06	-0.06	-0.06										-0.07					-0.06
				(0.06)	(0.08)	(0.08)	(0.08)										(0.08)					(0.08)
p(sdtemp)								-0.11	-0.11	-0.11										-0.12
								(0.18)	(0.18)	(0.18)										(0.18)
p(Meanhum)											0.01	0.01	0.01						0.01
											(0.01)	(0.01)	(0.01)						(0.01)
Log Likelihood	-73.03	-73.04	-73.06	-70.50	-73.18	-73.20	-73.22	-73.27	-73.27	-73.28	-73.29	-73.30	-73.30	-73.44	-73.44	-73.45	-74.78	-74.82	-74.97	-74.97	-75.16	-72.76
AICc	155.03	155.05	155.10	155.16	155.34	155.37	155.41	155.52	155.52	155.53	155.56	155.57	155.57	155.86	155.86	155.87	156.13	156.21	156.51	156.51	156.90	157.03
Delta	0.00	0.02	0.07	0.12	0.31	0.34	0.38	0.49	0.49	0.50	0.53	0.53	0.54	0.83	0.83	0.83	1.10	1.18	1.47	1.48	1.87	1.99
Weight	0.03	0.03	0.03	0.03	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.02	0.01	0.01	0.01	0.01	0.01
Num. obs.	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

Brazilian free-tailed bat (Tadarida brasiliensis)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8	Model 9
psi(Int)	1.11	0.96	0.84	0.87	0.90	0.69	0.69	0.93	0.96
	(0.60)	(0.58)	(0.62)	(0.63)	(0.52)	(0.53)	(0.53)	(0.58)	(0.58)
psi(Burn.intensity.basal)	10.37	0.82			21.39			1.58
	(8.19)	(0.83)			(18.95)
psi(Burn.intensity.Canopy)	-12.01		56.97		-28.59		34.71		1.26
	(9.25)		(208.37)		(24.56)		(92.31)		(1.94)
p(Int)	1.15	1.46*	1.34*	1.36*	3.91	3.85	3.95	4.78	4.64
	(0.76)	(0.71)	(0.66)	(0.66)	(2.72)	(2.42)	(2.43)	(2.54)	(2.64)
p(Meantemp)	-0.05	-0.08	-0.08	-0.08				-0.09	-0.08
	(0.06)	(0.05)	(0.05)	(0.05)				(0.05)	(0.05)
psi(Burn.intensity.soil)				42.80	3.70	33.60
				(221.42)	(3.66)	(180.26)
p(Julian)					-0.02	-0.02	-0.02	-0.02	-0.02
					(0.01)	(0.01)	(0.01)	(0.01)	(0.01)
p(Meanhum)					0.00
					(0.01)
Log Likelihood	-85.65	-87.30	-87.32	-87.35	-83.45	-87.70	-87.71	-86.47	-86.55
AICc	182.80	183.58	183.62	183.68	183.84	184.39	184.39	184.44	184.60
Delta	0.00	0.79	0.82	0.89	1.04	1.59	1.59	1.65	1.81
Weight	0.10	0.06	0.06	0.06	0.06	0.04	0.04	0.04	0.04
Num. obs.	46	46	46	46	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

hoary bat (Lasiurus cinereus)

Statistical models

	Model 1	Model 2
psi(Int)	0.76	0.85
	(0.47)	(0.52)
p(Int)	5.86	5.03
	(3.00)	(3.04)
p(Julian)	-0.03	-0.03
	(0.02)	(0.02)
p(Meanhum)		0.01
		(0.01)
Log Likelihood	-78.56	-77.89
AICc	163.68	164.75
Delta	0.00	1.07
Weight	0.09	0.05
Num. obs.	46	46
p < 0.001, p < 0.01, p < 0.05

Spotted bat (Euderma maculatum)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
psi(Int)	8.31	7.98	-0.45	-0.08	-0.36	5.89
	(89.85)	(65.83)	(2.28)	(1.17)	(2.35)	(21.66)
p(Int)	1.33	-0.23	1.59	4.76	1.58	5.63
	(2.14)	(2.60)	(2.14)	(4.55)	(2.15)	(6.45)
p(Meanhum)	-0.11	-0.14	-0.10	-0.27	-0.11	-0.11
	(0.06)	(0.07)	(0.06)	(0.19)	(0.06)	(0.06)
p(sdtemp)		0.62
		(0.50)
psi(Burn.intensity.basal)			0.68
			(0.90)
p(sdhum)				0.35
				(0.28)
psi(Burn.intensity.Canopy)					0.87
					(1.24)
p(Julian)						-0.02
						(0.03)
Log Likelihood	-10.76	-9.89	-10.50	-10.50	-10.52	-10.52
AICc	28.09	28.75	29.97	29.98	30.02	30.02
Delta	0.00	0.66	1.88	1.89	1.93	1.93
Weight	0.08	0.06	0.03	0.03	0.03	0.03
Num. obs.	46	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

western mastiff bat (Eumops perotis)

Statistical models

	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6	Model 7	Model 8
psi(Int)	-1.83	-1.25	3.25	7.75	-1.55	-0.94	2.31	3.93
	(1.69)	(1.25)	(3.34)	(10.64)	(1.50)	(1.57)	(3.90)	(4.07)
psi(Burn.intensity.basal)	-137.65	-90.37	73.50	216.34	-139.14	-82.45		108.19
	(94.08)	(67.03)	(86.62)	(274.45)	(91.83)	(51.80)		(119.13)
psi(Burn.intensity.Canopy)	137.00	90.09			138.17	83.54	95.68
	(92.80)	(65.42)			(90.24)	(51.62)	(113.69)
p(Int)	-0.55	0.65	-1.93***	-0.80	0.64	-1.88***	-2.03***	-2.50
	(1.75)	(1.53)	(0.45)	(1.15)	(1.62)	(0.46)	(0.44)	(1.40)
p(Meantemp)	-0.23	-0.17			-0.21			0.06
	(0.12)	(0.11)			(0.11)			(0.11)
p(sdtemp)	0.61
	(0.43)
psi(Burn.intensity.soil)			-53.48	-157.36			-69.47	-78.45
			(63.22)	(199.55)			(83.89)	(86.50)
p(Meanhum)				-0.01
				(0.02)
p(sdhum)					0.08
					(0.09)
Log Likelihood	-15.21	-16.90	-18.27	-17.03	-15.99	-18.63	-18.67	-17.53
AICc	44.58	45.31	45.45	45.56	46.13	46.17	46.24	46.56
Delta	0.00	0.73	0.87	0.98	1.55	1.59	1.66	1.98
Weight	0.09	0.06	0.06	0.05	0.04	0.04	0.04	0.03
Num. obs.	46	46	46	46	46	46	46	46
p < 0.001, p < 0.01, p < 0.05

Relationships between different species of Bats

Fire bats

## ~/Documents/new_bats/Rnew_bats/Bats_data_products/fire.asc has GDAL driver AAIGrid 
## and has 250 rows and 434 columns

## ~/Documents/new_bats/Rnew_bats/Bats_data_products/not_fire.asc has GDAL driver AAIGrid 
## and has 250 rows and 434 columns

	with.fire	without.fire	with.fire.sd
Yuma.Myotis	0.23	0.18	0.41
Small.Footed.Myotis	0.15	0.09	0.33
Little.Brown.Bat	0.13	0.21	0.26
Western.Red.Bat	0.12	0.53	0.31
Long.eared.Bat	0.22	0.65	0.39
Pallid.Bat	0.09	0.16	0.28
Townsend.s.big.eared.Bat	0.16	0.19	0.35
Big.Brow.Bat	0.21	0.35	0.39
Silver.Haired.Bat	0.18	0.49	0.36
Brazilian.free.tailed.bat	0.19	0.74	0.37
western.mastiff.bat	0.01	0.15	0.07

## 
##  One Sample t-test
## 
## data:  myyu
## t = 23.888, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.1818892
## 95 percent confidence interval:
##  0.2256631 0.2334887
## sample estimates:
## mean of x 
## 0.2295759

## 
##  One Sample t-test
## 
## data:  myci
## t = 36.501, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.09200702
## 95 percent confidence interval:
##  0.1482991 0.1546876
## sample estimates:
## mean of x 
## 0.1514933

## 
##  One Sample t-test
## 
## data:  mylu
## t = -57.863, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.2051132
## 95 percent confidence interval:
##  0.1304989 0.1353882
## sample estimates:
## mean of x 
## 0.1329435

## 
##  One Sample t-test
## 
## data:  labl
## t = -264.62, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.5267009
## 95 percent confidence interval:
##  0.1164016 0.1224351
## sample estimates:
## mean of x 
## 0.1194183

## 
##  One Sample t-test
## 
## data:  myev
## t = -229.73, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.6525984
## 95 percent confidence interval:
##  0.2128346 0.2202750
## sample estimates:
## mean of x 
## 0.2165548

## 
##  One Sample t-test
## 
## data:  anpa
## t = -52.936, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.1632505
## 95 percent confidence interval:
##  0.08879395 0.09411075
## sample estimates:
##  mean of x 
## 0.09145235

## 
##  One Sample t-test
## 
## data:  coto
## t = -21.478, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.1948568
## 95 percent confidence interval:
##  0.1550577 0.1617141
## sample estimates:
## mean of x 
## 0.1583859

## 
##  One Sample t-test
## 
## data:  epfu
## t = -75.202, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.3493452
## 95 percent confidence interval:
##  0.2018752 0.2093671
## sample estimates:
## mean of x 
## 0.2056211

## 
##  One Sample t-test
## 
## data:  lano
## t = -173.63, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.4905386
## 95 percent confidence interval:
##  0.1775398 0.1845274
## sample estimates:
## mean of x 
## 0.1810336

## 
##  One Sample t-test
## 
## data:  tabr
## t = -301.07, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.7393856
## 95 percent confidence interval:
##  0.1895036 0.1966171
## sample estimates:
## mean of x 
## 0.1930604

## 
##  One Sample t-test
## 
## data:  eupe
## t = -413.34, df = 41875, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0.1519613
## 95 percent confidence interval:
##  0.008627646 0.009980571
## sample estimates:
##   mean of x 
## 0.009304108

library(vioplot)

## Loading required package: sm
## Package 'sm', version 2.2-5.4: type help(sm) for summary information

vioplot(myyu,myci, mylu, labl, myev, anpa, coto, epfu, lano, tabr, eupe, col="grey")

The End

#valuetable <- getValues(AllLayers2)
#km1 <- kmeans(na.omit(valuetable), centers = 5, iter.max = 100, nstart = 10)
# create a blank raster with default values of 0
#rNA <- setValues(raster(AllLayers2), 0)
#for(i in 1:nlayers(AllLayers2)){
  #rNA[is.na(AllLayers2[[i]])] <- 1
#}
# convert rNA to an integer vector
#rNA <- getValues(rNA)
# convert valuetable to a data.frame
#valuetable <- as.data.frame(valuetable)
# if rNA is a 0, assign the cluster value at that position
#valuetable$class[rNA==0] <- km1$cluster
# if rNA is a 1, assign an NA at that position
#valuetable$class[rNA==1] <- NA
# create a blank raster
#classes1 <- raster(AllLayers2)
# assign values from the 'class' column of valuetable
#classes1 <- setValues(classes1, valuetable$class)
#plot(classes1, legend=TRUE, colNA="black")
#More info on how to do this clasification in *https://geoscripting-wur.github.io/AdvancedRasterAnalysis/*

Power Analysis

## 
## Call:
## occu(formula = ~Meanhum + 1 ~ 1, data = SimOccuMyVo2)
## 
## Occupancy:
##  Estimate    SE      z P(>|z|)
##     -0.33 0.731 -0.451   0.652
## 
## Detection:
##             Estimate     SE      z P(>|z|)
## (Intercept) -0.49767 1.1058 -0.450   0.653
## Meanhum     -0.00923 0.0144 -0.641   0.522
## 
## AIC: 85.66033

##                  0.025      0.975
## p(Int)     -2.66493507 1.66960349
## p(Meanhum) -0.03746837 0.01900186

## Profiling parameter 1 of 2 ... done.
## Profiling parameter 2 of 2 ... done.

##                  0.025      0.975
## p(Int)     -2.76769061 1.62413485
## p(Meanhum) -0.03895494 0.01837992

## Backtransformed linear combination(s) of Detection estimate(s)
## 
##  Estimate    SE LinComb (Intercept) Meanhum
##     0.377 0.258  -0.502           1     0.5
## 
## Transformation: logistic

##            Length             Class              Mode 
##                 1 unmarkedBackTrans                S4

##        0.05     0.95
##  0.09019551 0.786958

## Warning: Some observations have been discarded because corresponding
## covariates were missing.

## Warning: 8 sites have been discarded because of missing data.

## Warning: Some observations have been discarded because corresponding
## covariates were missing.

## Warning: 8 sites have been discarded because of missing data.

## [1] 10

## 
## --------------------------------------------------------------------------
## Evaluation of design K = 4 S = 62 (TS = 248)
## --------------------------------------------------------------------------
## estimator performance (excl empty histories)
## psi: bias = +0.0310   var = +0.0148   MSE = +0.0158
##   p: bias = -0.0087   var = +0.0102   MSE = +0.0103
##     covar = -0.0075 critA = +0.0261 critD = +1.063e-04
## estimator performance (excl also histories leading to boundary estimates)
## psi: bias = +0.0219   var = +0.0079   MSE = +0.0084
##   p: bias = -0.0055   var = +0.0095   MSE = +0.0095
##     covar = -0.0051 critA = +0.0179 critD = +5.378e-05
##  empty histories = 0.0%
##  boundary estimates = 1.2%
## this took  1.576 seconds 
## --------------------------------------------------------------------------

## $dist
##        [,1] [,2]   [,3]       [,4]        [,5]
##   [1,]   11   18 0.0168 0.22452662 0.323314803
##   [2,]    8   14 0.0155 0.15365326 0.367330550
##   [3,]    6    7 0.0084 0.27681552 0.101952333
##   [4,]    7   11 0.0235 0.14942362 0.296870257
##   [5,]    6   13 0.0023 0.10264751 0.510559198
##   [6,]   10   13 0.0121 0.30308670 0.172966754
##   [7,]   13   20 0.0091 0.28505105 0.282991625
##   [8,]   12   21 0.0097 0.23050924 0.367328088
##   [9,]    6   11 0.0121 0.11138423 0.398138526
##  [10,]    6    8 0.0142 0.17026449 0.189409474
##  [11,]   17   26 0.0008 0.37564141 0.279046843
##  [12,]   10   17 0.0185 0.19689844 0.348385401
##  [13,]   11   19 0.0135 0.21341619 0.358850470
##  [14,]   12   14 0.0019 0.55345996 0.102016630
##  [15,]    8   10 0.0146 0.27368013 0.147316041
##  [16,]    6   10 0.0150 0.12025816 0.335412443
##  [17,]    7    9 0.0149 0.21899815 0.165661171
##  [18,]   14   20 0.0050 0.34390571 0.234542948
##  [19,]   11   17 0.0193 0.23980349 0.285788874
##  [20,]    8   17 0.0035 0.13788019 0.497449536
##  [21,]   13   16 0.0030 0.47047257 0.137147180
##  [22,]    9   17 0.0111 0.16392186 0.418082881
##  [23,]   12   16 0.0066 0.34057947 0.189403215
##  [24,]   10   16 0.0218 0.20903637 0.308540833
##  [25,]   15   20 0.0014 0.42589996 0.189426297
##  [26,]   12   20 0.0115 0.24051946 0.335321201
##  [27,]    4    6 0.0064 0.09085934 0.265426361
##  [28,]    8   16 0.0067 0.14139524 0.456252348
##  [29,]   16   24 0.0011 0.36327383 0.266357280
##  [30,]    8   11 0.0217 0.21170026 0.209495206
##  [31,]    9   14 0.0265 0.19452864 0.290223943
##  [32,]   11   14 0.0078 0.35468138 0.159088653
##  [33,]   12   13 0.0004 0.98663540 0.053153054
##  [34,]   11   15 0.0126 0.29627610 0.204108286
##  [35,]    9   16 0.0166 0.17077992 0.377800298
##  [36,]    5    8 0.0114 0.10450134 0.308641291
##  [37,]    6    9 0.0173 0.13620539 0.266398992
##  [38,]    8   12 0.0271 0.18169610 0.266368078
##  [39,]    8   13 0.0225 0.16437782 0.318776995
##  [40,]    8    9 0.0049 0.46437881 0.078150915
##  [41,]   14   24 0.0045 0.27340452 0.353871992
##  [42,]    9   11 0.0099 0.33458757 0.132526116
##  [43,]   13   19 0.0087 0.30712173 0.249400328
##  [44,]   13   22 0.0081 0.25681489 0.345460150
##  [45,]    4    5 0.0048 0.13692652 0.147275280
##  [46,]   11   20 0.0082 0.20537835 0.392717080
##  [47,]   11   16 0.0187 0.26187041 0.246388971
##  [48,]   10   18 0.0124 0.18803852 0.386041268
##  [49,]    9   18 0.0053 0.15890691 0.456408635
##  [50,]    7   12 0.0178 0.13683439 0.353740348
##  [51,]   12   18 0.0135 0.27242819 0.266428446
##  [52,]   10   15 0.0230 0.22706994 0.266448455
##  [53,]    6   12 0.0055 0.10603610 0.456406392
##  [54,]    7   15 0.0025 0.12021707 0.503058970
##  [55,]    5    9 0.0075 0.09400784 0.385927448
##  [56,]   12   17 0.0123 0.29947289 0.228941738
##  [57,]   11   21 0.0060 0.19922504 0.425185283
##  [58,]    3    6 0.0019 0.05300865 0.456372065
##  [59,]    5    7 0.0108 0.12757896 0.221400144
##  [60,]    3    3 0.0014 1.00000000 0.012096774
##  [61,]   15   26 0.0019 0.29027207 0.361130577
##  [62,]    5   11 0.0017 0.08510886 0.521076113
##  [63,]    8   15 0.0108 0.14645019 0.413210870
##  [64,]    9   13 0.0233 0.21678472 0.241739377
##  [65,]   15   19 0.0013 0.49172410 0.155752621
##  [66,]   12   19 0.0147 0.25382091 0.301834517
##  [67,]    9   12 0.0166 0.25534831 0.189575780
##  [68,]   13   18 0.0064 0.33897054 0.214155235
##  [69,]   13   21 0.0092 0.26899419 0.314836288
##  [70,]   10   14 0.0189 0.25513422 0.221318134
##  [71,]    2    3 0.0008 0.04543110 0.266305228
##  [72,]   16   28 0.0016 0.30723307 0.367365899
##  [73,]    6   14 0.0012 0.10052001 0.561841302
##  [74,]   14   22 0.0065 0.29897145 0.296794096
##  [75,]   12   24 0.0020 0.21207247 0.456215194
##  [76,]   14   25 0.0033 0.26473166 0.380753640
##  [77,]    9   21 0.0005 0.15075108 0.561834164
##  [78,]   12   22 0.0057 0.22260995 0.398276496
##  [79,]    9   15 0.0204 0.18039585 0.335309322
##  [80,]    4    8 0.0029 0.07066999 0.456206586
##  [81,]   10   22 0.0008 0.17026940 0.521078617
##  [82,]   13   15 0.0008 0.63834760 0.094764169
##  [83,]   16   23 0.0014 0.38869900 0.238559474
##  [84,]    7   10 0.0201 0.17192187 0.234573551
##  [85,]   15   23 0.0034 0.33035075 0.280689739
##  [86,]    9   10 0.0027 0.57719723 0.069838114
##  [87,]   14   23 0.0046 0.28455066 0.325980193
##  [88,]    7   20 0.0001 0.11376986 0.709156825
##  [89,]   10   19 0.0090 0.18155868 0.421805504
##  [90,]   14   21 0.0054 0.31781398 0.266448182
##  [91,]    7   13 0.0118 0.12887761 0.406942551
##  [92,]   17   30 0.0004 0.32434912 0.372983805
##  [93,]   12   15 0.0046 0.41056052 0.147318715
##  [94,]    1    2 0.0001 0.01766963 0.456484625
##  [95,]    4    7 0.0048 0.07679513 0.367546391
##  [96,]    9   19 0.0015 0.15546292 0.492907481
##  [97,]    7   14 0.0073 0.12373890 0.456442535
##  [98,]   11   22 0.0037 0.19438207 0.456402507
##  [99,]    7    8 0.0053 0.36480763 0.088405054
## [100,]   13   23 0.0058 0.24750599 0.374645093
## [101,]   14   18 0.0015 0.43805504 0.165678534
## [102,]   10   12 0.0061 0.40136511 0.120558967
## [103,]   14   26 0.0023 0.25773347 0.406709172
## [104,]   15   31 0.0001 0.26133983 0.478491389
## [105,]   17   28 0.0008 0.34471097 0.327540753
## [106,]    1    1 0.0003 1.00000000 0.004032258
## [107,]   15   25 0.0033 0.30061932 0.335393779
## [108,]    4    9 0.0013 0.06762562 0.536685098
## [109,]   17   23 0.0004 0.46573612 0.199047562
## [110,]   15   29 0.0005 0.26957139 0.433753934
## [111,]    5   10 0.0038 0.08831069 0.456447998
## [112,]    5    6 0.0077 0.20071554 0.120553856
## [113,]    7    7 0.0014 1.00000000 0.028225806
## [114,]   14   27 0.0009 0.25207379 0.431944947
## [115,]   10   21 0.0018 0.17310340 0.489276407
## [116,]    3    4 0.0023 0.08510598 0.189577897
## [117,]   12   23 0.0035 0.21682526 0.427818610
## [118,]   16   22 0.0006 0.42343824 0.209490427
## [119,]   12   25 0.0011 0.20836008 0.483787000
## [120,]   10   11 0.0015 0.70222585 0.063152222
## [121,]    4    4 0.0021 1.00000000 0.016129032
## [122,]    5    5 0.0010 1.00000000 0.020161290
## [123,]   17   32 0.0003 0.31036046 0.415846762
## [124,]   11   23 0.0010 0.19066611 0.486364326
## [125,]    3    8 0.0001 0.04908538 0.657482614
## [126,]   17   25 0.0005 0.39769141 0.253463818
## [127,]   13   17 0.0050 0.38773399 0.176805026
## [128,]    9   20 0.0007 0.15273405 0.527874018
## [129,]   15   22 0.0024 0.35245447 0.251647544
## [130,]   14   19 0.0020 0.38100614 0.201063680
## [131,]   11   13 0.0031 0.47452880 0.110444190
## [132,]   16   26 0.0018 0.32895778 0.318760744
## [133,]    9    9 0.0007 1.00000000 0.036290323
## [134,]    3    7 0.0003 0.05022243 0.561871836
## [135,]   20   27 0.0001 0.55128443 0.197552156
## [136,]    9   22 0.0001 0.14919924 0.594665446
## [137,]   11   24 0.0006 0.18768233 0.515639224
## [138,]   18   26 0.0003 0.43362577 0.241766152
## [139,]    7   16 0.0010 0.11781088 0.547414033
## [140,]    6    6 0.0025 1.00000000 0.024193548
## [141,]    8    8 0.0013 1.00000000 0.032258065
## [142,]   13   26 0.0013 0.22972210 0.456282298
## [143,]   15   30 0.0002 0.26505976 0.456327633
## [144,]   14   17 0.0007 0.53430306 0.128314902
## [145,]   17   27 0.0007 0.35850592 0.303687196
## [146,]    3    5 0.0013 0.06009815 0.335485005
## [147,]   15   27 0.0014 0.28190827 0.385994888
## [148,]   18   31 0.0002 0.35021189 0.356964028
## [149,]   18   29 0.0002 0.37351872 0.313087763
## [150,]   16   25 0.0017 0.34398051 0.293100674
## [151,]   15   24 0.0036 0.31364246 0.308595169
## [152,]    8   18 0.0015 0.13525324 0.536629536
## [153,]   17   22 0.0001 0.52194785 0.169925150
## [154,]   13   24 0.0027 0.24011773 0.402867929
## [155,]   10   20 0.0027 0.17669887 0.456345078
## [156,]    2    4 0.0003 0.03532969 0.456304219
## [157,]   16   27 0.0008 0.31695999 0.343549308
## [158,]   15   21 0.0012 0.38251227 0.221464229
## [159,]   16   29 0.0004 0.29937922 0.390620531
## [160,]   17   24 0.0007 0.42679485 0.226759005
## [161,]   14   28 0.0002 0.24741321 0.456344268
## [162,]   13   25 0.0023 0.23437686 0.430032162
## [163,]   15   18 0.0004 0.60239444 0.120436207
## [164,]   15   28 0.0006 0.27519917 0.410221285
## [165,]   11   12 0.0006 0.83791891 0.057766725
## [166,]    2    2 0.0005 1.00000000 0.008064516
## [167,]    5   13 0.0003 0.08203616 0.639079748
## [168,]   19   27 0.0001 0.47125747 0.231027034
## [169,]   16   21 0.0008 0.47282254 0.179137799
## [170,]    6   15 0.0004 0.09910886 0.610637684
## [171,]   11   11 0.0001 1.00000000 0.044354839
## [172,]    2    5 0.0002 0.03302487 0.610640212
## [173,]   12   29 0.0001 0.19936828 0.586559302
## [174,]   18   30 0.0006 0.36066953 0.335321309
## [175,]   13   27 0.0008 0.22595535 0.481784272
## [176,]   18   32 0.0001 0.34153464 0.377888136
## [177,]   13   14 0.0001 1.00000000 0.056451613
## [178,]   14   16 0.0002 0.72911735 0.088482574
## [179,]    7   18 0.0001 0.11503092 0.630893043
## [180,]   12   26 0.0003 0.20530490 0.510634339
## [181,]   17   29 0.0006 0.33344069 0.350621546
## [182,]   19   29 0.0001 0.42108035 0.277624695
## [183,]   12   27 0.0002 0.20289593 0.536513755
## [184,]   14   30 0.0002 0.24041393 0.503033379
## [185,]   16   30 0.0004 0.29282014 0.413250184
## [186,]   20   30 0.0001 0.45408532 0.266343361
## [187,]   17   21 0.0001 0.60716623 0.139481193
## [188,]   10   10 0.0002 1.00000000 0.040322581
## [189,]    6   16 0.0001 0.09813565 0.657551647
## [190,]   13   29 0.0002 0.22037087 0.530679657
## [191,]    5   12 0.0005 0.08319267 0.581545155
## [192,]   19   26 0.0001 0.50790235 0.206386984
## [193,]   10   23 0.0001 0.16804222 0.551718006
## [194,]   18   27 0.0002 0.40884225 0.266290847
## [195,]   17   33 0.0001 0.30498893 0.436340091
## [196,]   18   25 0.0001 0.46639830 0.216092923
## [197,]    7   17 0.0002 0.11619216 0.589896956
## [198,]   18   28 0.0004 0.38924195 0.290088806
## [199,]   20   34 0.0001 0.39354030 0.348300999
## [200,]   19   32 0.0001 0.37695198 0.342237148
## [201,]    4   11 0.0001 0.06521474 0.680227931
## [202,]    4   10 0.0004 0.06599048 0.610636843
## [203,]    8   19 0.0001 0.13337892 0.574232112
## [204,]   20   35 0.0001 0.38406358 0.367423642
## [205,]   22   32 0.0001 0.52398447 0.246280754
## [206,]   16   34 0.0001 0.27571023 0.497370046
## [207,]   18   36 0.0001 0.31821365 0.456263283
## [208,]   18   23 0.0001 0.57428424 0.161588753
## [209,]   21   34 0.0001 0.43342594 0.316365050
## [210,]   19   34 0.0001 0.35862602 0.382214904
## [211,]   10   24 0.0001 0.16640443 0.581549235
## [212,]   16   20 0.0001 0.54739008 0.147306892
## [213,]   19   31 0.0001 0.38898337 0.321408794
## [214,]   13   28 0.0001 0.22286876 0.506517918
## [215,]   19   30 0.0001 0.40340729 0.299863283
## [216,]   14   29 0.0001 0.24359192 0.480005463
## [217,]   12   12 0.0001 1.00000000 0.048387097
## [218,]   18   24 0.0001 0.51085751 0.189456468
## 
## $biaspsi
## [1] 0.03102304
## 
## $varpsi
## [1] 0.014844
## 
## $MSEpsi
## [1] 0.01580643
## 
## $biasp
## [1] -0.008716855
## 
## $varp
## [1] 0.01019557
## 
## $MSEp
## [1] 0.01027155
## 
## $covar
## [1] -0.007486403
## 
## $critA
## [1] 0.02607799
## 
## $critD
## [1] 0.0001063104
## 
## $biaspsi_B
## [1] 0.02191949
## 
## $varpsi_B
## [1] 0.007936446
## 
## $MSEpsi_B
## [1] 0.00841691
## 
## $biasp_B
## [1] -0.005538184
## 
## $varp_B
## [1] 0.009451622
## 
## $MSEp_B
## [1] 0.009482293
## 
## $covar_B
## [1] -0.005101769
## 
## $critA_B
## [1] 0.0178992
## 
## $critD_B
## [1] 5.378356e-05
## 
## $pempty
## [1] 0
## 
## $pbound
## [1] 1.17